{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorflow Version: 2.0.0-alpha0\n"
     ]
    }
   ],
   "source": [
    "print('Tensorflow Version: {}'.format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./dataset/Income1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>26.658839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.401338</td>\n",
       "      <td>27.306435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10.842809</td>\n",
       "      <td>22.132410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11.244147</td>\n",
       "      <td>21.169841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>11.645485</td>\n",
       "      <td>15.192634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>12.086957</td>\n",
       "      <td>26.398951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>12.488294</td>\n",
       "      <td>17.435307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>12.889632</td>\n",
       "      <td>25.507885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>13.290970</td>\n",
       "      <td>36.884595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>13.732441</td>\n",
       "      <td>39.666109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>14.133779</td>\n",
       "      <td>34.396281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>14.535117</td>\n",
       "      <td>41.497994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>14.976589</td>\n",
       "      <td>44.981575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>15.377926</td>\n",
       "      <td>47.039595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>15.779264</td>\n",
       "      <td>48.252578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>16.220736</td>\n",
       "      <td>57.034251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>16.622074</td>\n",
       "      <td>51.490919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>17.023411</td>\n",
       "      <td>61.336621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>17.464883</td>\n",
       "      <td>57.581988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>17.866221</td>\n",
       "      <td>68.553714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>18.267559</td>\n",
       "      <td>64.310925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>18.709030</td>\n",
       "      <td>68.959009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>19.110368</td>\n",
       "      <td>74.614639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>19.511706</td>\n",
       "      <td>71.867195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>19.913043</td>\n",
       "      <td>76.098135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>20.354515</td>\n",
       "      <td>75.775218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>20.755853</td>\n",
       "      <td>72.486055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>21.157191</td>\n",
       "      <td>77.355021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>21.598662</td>\n",
       "      <td>72.118790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>80.260571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Education     Income\n",
       "0            1  10.000000  26.658839\n",
       "1            2  10.401338  27.306435\n",
       "2            3  10.842809  22.132410\n",
       "3            4  11.244147  21.169841\n",
       "4            5  11.645485  15.192634\n",
       "5            6  12.086957  26.398951\n",
       "6            7  12.488294  17.435307\n",
       "7            8  12.889632  25.507885\n",
       "8            9  13.290970  36.884595\n",
       "9           10  13.732441  39.666109\n",
       "10          11  14.133779  34.396281\n",
       "11          12  14.535117  41.497994\n",
       "12          13  14.976589  44.981575\n",
       "13          14  15.377926  47.039595\n",
       "14          15  15.779264  48.252578\n",
       "15          16  16.220736  57.034251\n",
       "16          17  16.622074  51.490919\n",
       "17          18  17.023411  61.336621\n",
       "18          19  17.464883  57.581988\n",
       "19          20  17.866221  68.553714\n",
       "20          21  18.267559  64.310925\n",
       "21          22  18.709030  68.959009\n",
       "22          23  19.110368  74.614639\n",
       "23          24  19.511706  71.867195\n",
       "24          25  19.913043  76.098135\n",
       "25          26  20.354515  75.775218\n",
       "26          27  20.755853  72.486055\n",
       "27          28  21.157191  77.355021\n",
       "28          29  21.598662  72.118790\n",
       "29          30  22.000000  80.260571"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1da636d3a20>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.Education, data.Income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.Education\n",
    "y = data.Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(tf.keras.layers.Dense(1, input_shape=(1,)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()   # ax + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='mse'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5000\n",
      "30/30 [==============================] - 0s 2ms/sample - loss: 6073.6528\n",
      "Epoch 2/5000\n",
      "30/30 [==============================] - 0s 327us/sample - loss: 6070.9722\n",
      "Epoch 3/5000\n",
      "30/30 [==============================] - 0s 53us/sample - loss: 6068.2915\n",
      "Epoch 4/5000\n",
      "30/30 [==============================] - 0s 32us/sample - loss: 6065.6123\n",
      "Epoch 5/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6062.9351\n",
      "Epoch 6/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 6060.2568\n",
      "Epoch 7/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6057.5796\n",
      "Epoch 8/5000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 6054.9033\n",
      "Epoch 9/5000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 6052.2275\n",
      "Epoch 10/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6049.5527\n",
      "Epoch 11/5000\n",
      "30/30 [==============================] - 0s 32us/sample - loss: 6046.8779\n",
      "Epoch 12/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6044.2041\n",
      "Epoch 13/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6041.5312\n",
      "Epoch 14/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 6038.8589\n",
      "Epoch 15/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6036.1875\n",
      "Epoch 16/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6033.5166\n",
      "Epoch 17/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 6030.8467\n",
      "Epoch 18/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6028.1772\n",
      "Epoch 19/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6025.5088\n",
      "Epoch 20/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 6022.8408\n",
      "Epoch 21/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6020.1738\n",
      "Epoch 22/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6017.5073\n",
      "Epoch 23/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6014.8428\n",
      "Epoch 24/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 6012.1782\n",
      "Epoch 25/5000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 6009.5146\n",
      "Epoch 26/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 6006.8516\n",
      "Epoch 27/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6004.1895\n",
      "Epoch 28/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 6001.5283\n",
      "Epoch 29/5000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 5998.8682\n",
      "Epoch 30/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5996.2080\n",
      "Epoch 31/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5993.5488\n",
      "Epoch 32/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5990.8911\n",
      "Epoch 33/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5988.2344\n",
      "Epoch 34/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5985.5781\n",
      "Epoch 35/5000\n",
      "30/30 [==============================] - 0s 0s/sample - loss: 5982.9229\n",
      "Epoch 36/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 5980.2681\n",
      "Epoch 37/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5977.6143\n",
      "Epoch 38/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 5974.9624\n",
      "Epoch 39/5000\n",
      "30/30 [==============================] - 0s 32us/sample - loss: 5972.3101\n",
      "Epoch 40/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5969.6582\n",
      "Epoch 41/5000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 5967.0083\n",
      "Epoch 42/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5964.3594\n",
      "Epoch 43/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 5961.7109\n",
      "Epoch 44/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5959.0635\n",
      "Epoch 45/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5956.4165\n",
      "Epoch 46/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5953.7705\n",
      "Epoch 47/5000\n",
      "30/30 [==============================] - 0s 0s/sample - loss: 5951.1255\n",
      "Epoch 48/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5948.4810\n",
      "Epoch 49/5000\n",
      "30/30 [==============================] - 0s 66us/sample - loss: 5945.8379\n",
      "Epoch 50/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5943.1953\n",
      "Epoch 51/5000\n",
      "30/30 [==============================] - 0s 32us/sample - loss: 5940.5542\n",
      "Epoch 52/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5937.9136\n",
      "Epoch 53/5000\n",
      "30/30 [==============================] - 0s 67us/sample - loss: 5935.2739\n",
      "Epoch 54/5000\n",
      "30/30 [==============================] - 0s 34us/sample - loss: 5932.6353\n",
      "Epoch 55/5000\n",
      "30/30 [==============================] - 0s 32us/sample - loss: 5929.9976\n",
      "Epoch 56/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5927.3599\n",
      "Epoch 57/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5924.7241\n",
      "Epoch 58/5000\n",
      "30/30 [==============================] - 0s 33us/sample - loss: 5922.0884\n",
      "Epoch 59/5000\n",
      "30/30 [==============================] - 0s 100us/sample - loss: 5919.4536\n",
      "Epoch 60/5000\n",
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     ]
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     ]
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     ]
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     ]
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    },
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     ]
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     ]
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     ]
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     "text": [
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     ]
    },
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     ]
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     ]
    },
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     ]
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     ]
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     ]
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     ]
    },
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     "text": [
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     ]
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     ]
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     ]
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     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4994/5000\n",
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      "Epoch 4999/5000\n",
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      "Epoch 5000/5000\n",
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     ]
    }
   ],
   "source": [
    "history = model.fit(x, y, epochs=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27.074318],\n",
       "       [28.011703],\n",
       "       [29.042831],\n",
       "       [29.980217],\n",
       "       [30.917603],\n",
       "       [31.948729],\n",
       "       [32.886112],\n",
       "       [33.8235  ],\n",
       "       [34.760887],\n",
       "       [35.79201 ],\n",
       "       [36.7294  ],\n",
       "       [37.666786],\n",
       "       [38.69791 ],\n",
       "       [39.635296],\n",
       "       [40.572685],\n",
       "       [41.603806],\n",
       "       [42.541195],\n",
       "       [43.47858 ],\n",
       "       [44.50971 ],\n",
       "       [45.44709 ],\n",
       "       [46.38448 ],\n",
       "       [47.415604],\n",
       "       [48.352993],\n",
       "       [49.290375],\n",
       "       [50.227764],\n",
       "       [51.25889 ],\n",
       "       [52.196278],\n",
       "       [53.13366 ],\n",
       "       [54.164783],\n",
       "       [55.102173]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[50.430862]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(pd.Series([20]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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